Jeremias Sulam | Technion Israel Institute of Technology (original) (raw)

Papers by Jeremias Sulam

Research paper thumbnail of Large Inpainting of Face Images With Trainlets

IEEE Signal Processing Letters, 2016

Image inpainting is concerned with the completion of missing data in an image. When the area to i... more Image inpainting is concerned with the completion of missing data in an image. When the area to inpaint is relatively large, this problem becomes challenging. In these cases, traditional methods based on patch models and image propagation are limited, since they fail to consider a global perspective of the problem. In this work, we employ a recently proposed dictionary learning framework, coined Trainlets, to design large adaptable atoms from a corpus of various datasets of face images by leveraging the Online Sparse Dictionary Learning algorithm. We therefore formulate the inpainting task as an inverse problem with a sparse-promoting prior based on the learned global model. Our results show the effectiveness of our scheme, obtaining much more plausible results than competitive methods.

Research paper thumbnail of Gaussian mixture diffusion

2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016

Research paper thumbnail of Trainlets: Dictionary Learning in High Dimensions

IEEE Transactions on Signal Processing, 2016

Research paper thumbnail of Dynamical System Classification with Diffusion Embedding for ECG-Based Person Identification

Research paper thumbnail of Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015

Research paper thumbnail of Simultaneous Ultrasound Harmonic Imaging Fusion and Clutter Removal with Sparse Signal Separation

In ultrasound, second harmonic imaging is usually preferred due to the higher clutter artifacts a... more In ultrasound, second harmonic imaging is usually preferred due to the higher clutter artifacts and speckle noise common in the fundamental frequency image. Typical approaches use either one or the other image, applying corresponding filters for each case. In this work we propose a method based on a joint sparsity model that fuses the first and second harmonic images while performing clutter mitigation and noise reduction. Our approach, Fused-MCA, uses two adaptive dictionaries for characterizing the clutter components in each image, and a common dictionary for the tissue representation. Our results indicate that the resulting images contain less clutter artifacts, less speckle noise and present some of the benefits of both harmonic input images.

Research paper thumbnail of Nonlinear slight parameter changes detection: a forecasting approach

Research paper thumbnail of ImageDenoisingthroughMulti-ScaleLearntDictionaries

Research paper thumbnail of Expected Patch Log Likelihood with a Sparse Prior

Lecture Notes in Computer Science, 2015

Image priors are of great importance in image restoration tasks. These problems can be addressed ... more Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this idea has been shown to lead to state-of-the-art results in image denoising and debluring. In this paper we combine the EPLL with a sparse-representation prior. Our derivation leads to a close yet extended variant of the popular K-SVD image denoising algorithm, where in order to effectively maximize the EPLL the denoising process should be iterated. This concept lies at the core of the K-SVD formulation, but has not been addressed before due the need to set different denoising thresholds in the successive sparse coding stages. We present a method that intrinsically determines these thresholds in order to improve the image estimate. Our results show a notable improvement over K-SVD in image denoising and inpainting, achieving comparable performance to that of EPLL with GMM in denoising.

Research paper thumbnail of Image denoising through multi-scale learnt dictionaries

2014 IEEE International Conference on Image Processing (ICIP), 2014

Over the last decade, a number of algorithms have shown promising results in removing additive wh... more Over the last decade, a number of algorithms have shown promising results in removing additive white Gaussian noise from natural images, and though different, they all share in common a patch based strategy by locally denoising overlapping patches. While this lowers the complexity of the problem, it also causes noticeable artifacts when dealing with large smooth areas. In this paper we present a patch-based denoising algorithm relying on a sparsity-inspired model (K-SVD), which uses a multi-scale analysis framework. This allows us to overcome some of the disadvantages of the popular algorithms. We look for a sparse representation under an already sparsifying wavelet transform by adaptively training a dictionary on the different decomposition bands of the noisy image itself, leading to a multi-scale version of the K-SVD algorithm. We then combine the single scale and multi-scale approaches by merging both outputs by weighted joint sparse coding of the images. Our experiments on natural images indicate that our method is competitive with state of the art algorithms in terms of PSNR while giving superior results with respect to visual quality.

Research paper thumbnail of Nonlinear slight parameter changes detection: a forecasting approach

Research paper thumbnail of Sulam, J., Ophir, B., Elad M, Image Denoising Through Multi-Scale Learnt Dictionaries, International Conference on Image Processing ICIP, November 2014 (oral presentation).

Over the last decade, a number of algorithms have shown promising results in removing additive wh... more Over the last decade, a number of algorithms have shown promising results in removing additive white Gaussian noise from natural images, and though different, they all share in common a patch based strategy by locally denoising overlapping patches. While this lowers the complexity of the problem, it also causes noticeable artifacts when dealing with large smooth areas. In this paper we present a patch-based denoising algorithm relying on a sparsity-inspired model (K-SVD), which uses a multi-scale analysis framework. This allows us to overcome some of the disadvantages of the popular algorithms. We look for a sparse representation under an already sparsifying wavelet transform by adaptively training a dictionary on the different decomposition bands of the noisy image itself, leading to a multi-scale version of the K-SVD algorithm. We then combine the single scale and multi-scale approaches by merging both outputs by weighted joint sparse coding of the images. Our experiments on natural images indicate that our method is competitive with state of the art agorithms in terms of PSNR while giving superior results with respect to visual quality.

Research paper thumbnail of Sulam, J. and Elad M, Expected Patch Log Likelihood with a Sparse Prior, Energy Minimization Methods in Computer Vision and Pattern Recognition EMMCVPR, Januray 2015 (oral presentation).

Image priors are of great importance in image restoration tasks. These problems can be addressed ... more Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this idea has been shown to lead to state-of-the-art results in image denoising and debluring. In this paper we combine the EPLL with a sparse-representation prior. Our derivation leads to a close yet extended variant of the popular K-SVD image denoising algorithm, where in order to effectively maximize the EPLL the denoising process should be iterated. This concept lies at the core of the K-SVD formulation, but has not been addressed before due the need to set different denoising thresholds in the successive sparse coding stages. We present a method that intrinsically determines these thresholds in order to improve the image estimate. Our results show a notable improvement over K-SVD in image denoising and inpainting, achieving comparable performance to that of EPLL with GMM in denoising.

Research paper thumbnail of Large Inpainting of Face Images With Trainlets

IEEE Signal Processing Letters, 2016

Image inpainting is concerned with the completion of missing data in an image. When the area to i... more Image inpainting is concerned with the completion of missing data in an image. When the area to inpaint is relatively large, this problem becomes challenging. In these cases, traditional methods based on patch models and image propagation are limited, since they fail to consider a global perspective of the problem. In this work, we employ a recently proposed dictionary learning framework, coined Trainlets, to design large adaptable atoms from a corpus of various datasets of face images by leveraging the Online Sparse Dictionary Learning algorithm. We therefore formulate the inpainting task as an inverse problem with a sparse-promoting prior based on the learned global model. Our results show the effectiveness of our scheme, obtaining much more plausible results than competitive methods.

Research paper thumbnail of Gaussian mixture diffusion

2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016

Research paper thumbnail of Trainlets: Dictionary Learning in High Dimensions

IEEE Transactions on Signal Processing, 2016

Research paper thumbnail of Dynamical System Classification with Diffusion Embedding for ECG-Based Person Identification

Research paper thumbnail of Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015

Research paper thumbnail of Simultaneous Ultrasound Harmonic Imaging Fusion and Clutter Removal with Sparse Signal Separation

In ultrasound, second harmonic imaging is usually preferred due to the higher clutter artifacts a... more In ultrasound, second harmonic imaging is usually preferred due to the higher clutter artifacts and speckle noise common in the fundamental frequency image. Typical approaches use either one or the other image, applying corresponding filters for each case. In this work we propose a method based on a joint sparsity model that fuses the first and second harmonic images while performing clutter mitigation and noise reduction. Our approach, Fused-MCA, uses two adaptive dictionaries for characterizing the clutter components in each image, and a common dictionary for the tissue representation. Our results indicate that the resulting images contain less clutter artifacts, less speckle noise and present some of the benefits of both harmonic input images.

Research paper thumbnail of Nonlinear slight parameter changes detection: a forecasting approach

Research paper thumbnail of ImageDenoisingthroughMulti-ScaleLearntDictionaries

Research paper thumbnail of Expected Patch Log Likelihood with a Sparse Prior

Lecture Notes in Computer Science, 2015

Image priors are of great importance in image restoration tasks. These problems can be addressed ... more Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this idea has been shown to lead to state-of-the-art results in image denoising and debluring. In this paper we combine the EPLL with a sparse-representation prior. Our derivation leads to a close yet extended variant of the popular K-SVD image denoising algorithm, where in order to effectively maximize the EPLL the denoising process should be iterated. This concept lies at the core of the K-SVD formulation, but has not been addressed before due the need to set different denoising thresholds in the successive sparse coding stages. We present a method that intrinsically determines these thresholds in order to improve the image estimate. Our results show a notable improvement over K-SVD in image denoising and inpainting, achieving comparable performance to that of EPLL with GMM in denoising.

Research paper thumbnail of Image denoising through multi-scale learnt dictionaries

2014 IEEE International Conference on Image Processing (ICIP), 2014

Over the last decade, a number of algorithms have shown promising results in removing additive wh... more Over the last decade, a number of algorithms have shown promising results in removing additive white Gaussian noise from natural images, and though different, they all share in common a patch based strategy by locally denoising overlapping patches. While this lowers the complexity of the problem, it also causes noticeable artifacts when dealing with large smooth areas. In this paper we present a patch-based denoising algorithm relying on a sparsity-inspired model (K-SVD), which uses a multi-scale analysis framework. This allows us to overcome some of the disadvantages of the popular algorithms. We look for a sparse representation under an already sparsifying wavelet transform by adaptively training a dictionary on the different decomposition bands of the noisy image itself, leading to a multi-scale version of the K-SVD algorithm. We then combine the single scale and multi-scale approaches by merging both outputs by weighted joint sparse coding of the images. Our experiments on natural images indicate that our method is competitive with state of the art algorithms in terms of PSNR while giving superior results with respect to visual quality.

Research paper thumbnail of Nonlinear slight parameter changes detection: a forecasting approach

Research paper thumbnail of Sulam, J., Ophir, B., Elad M, Image Denoising Through Multi-Scale Learnt Dictionaries, International Conference on Image Processing ICIP, November 2014 (oral presentation).

Over the last decade, a number of algorithms have shown promising results in removing additive wh... more Over the last decade, a number of algorithms have shown promising results in removing additive white Gaussian noise from natural images, and though different, they all share in common a patch based strategy by locally denoising overlapping patches. While this lowers the complexity of the problem, it also causes noticeable artifacts when dealing with large smooth areas. In this paper we present a patch-based denoising algorithm relying on a sparsity-inspired model (K-SVD), which uses a multi-scale analysis framework. This allows us to overcome some of the disadvantages of the popular algorithms. We look for a sparse representation under an already sparsifying wavelet transform by adaptively training a dictionary on the different decomposition bands of the noisy image itself, leading to a multi-scale version of the K-SVD algorithm. We then combine the single scale and multi-scale approaches by merging both outputs by weighted joint sparse coding of the images. Our experiments on natural images indicate that our method is competitive with state of the art agorithms in terms of PSNR while giving superior results with respect to visual quality.

Research paper thumbnail of Sulam, J. and Elad M, Expected Patch Log Likelihood with a Sparse Prior, Energy Minimization Methods in Computer Vision and Pattern Recognition EMMCVPR, Januray 2015 (oral presentation).

Image priors are of great importance in image restoration tasks. These problems can be addressed ... more Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this idea has been shown to lead to state-of-the-art results in image denoising and debluring. In this paper we combine the EPLL with a sparse-representation prior. Our derivation leads to a close yet extended variant of the popular K-SVD image denoising algorithm, where in order to effectively maximize the EPLL the denoising process should be iterated. This concept lies at the core of the K-SVD formulation, but has not been addressed before due the need to set different denoising thresholds in the successive sparse coding stages. We present a method that intrinsically determines these thresholds in order to improve the image estimate. Our results show a notable improvement over K-SVD in image denoising and inpainting, achieving comparable performance to that of EPLL with GMM in denoising.