Compressive Sensing on a CMOS Separable-Transform Image Sensor (original) (raw)

IJERT-Focus on Compressive Sensing in a Single Pixel Camera

International Journal of Engineering Research and Technology (IJERT), 2013

https://www.ijert.org/focus-on-compressive-sensing-in-a-single-pixel-camera https://www.ijert.org/research/focus-on-compressive-sensing-in-a-single-pixel-camera-IJERTV2IS80769.pdf Digital cameras are made from semiconductor. The semiconductor material of choice for large-scale electronics integration (silicon) also happens to readily convert photons at visual wavelengths into electrons. All digital cameras are mega-pixels in range. When we are using this many no of pixels the size will be increased and also complexly is more. When ever size and complexity are increases cost will be increase. Ex:-if we use 5 Mega-Pixel camera "we will get better quality image than 2 Mega-Pixel camera image" .So, based on this condition if we want better quality image we have to add the few more components(pixels).if we add components automatically size will be increases, complexity is more and cost is high. In this Project, a new approach to building simpler, smaller and cheaper digital cameras that can operate efficiently across a much broader spectral range than conventional silicon-based cameras is studied. Our approach fuses a new camera architecture based on a digital micro mirror device (DMD) with the new mathematical theory and algorithms of compressive sampling. CS combines sampling and compression into a single non adaptive linear measurement process. Rather than measuring pixel samples of the scene under view, measure inner products between the scene and a set of test functions. Interestingly, random test functions play a key role, making each measurement a random sum of pixel values taken across the entire image. When the scene under view is compressible by An algorithm like JPEG or JPEG2000, the CS theory enables us to stably reconstruct an image of the scene from fewer measurements than the number of reconstructed pixels. In this manner sub-Nyquist image acquisition is achieved. In this Project Compressive Sensing algorithms are studied. Compression and Decompression process also studied using different algorithms such as DCT and Haar wavelet Algorithms.

Various Applications of Compressive Sensing in Digital Image Processing: A Survey

Compressive sensing (CS) is a fast growing area of research. It neglects the extravagant acquisition process by measuring lesser values to reconstruct the image or signal. Compressive sensing is adopted successfully in various fields of image processing and proved its efficiency. Some of the image processing applications like face recognition, video encoding, Image encryption and reconstruction are presented here.

Compressive Sensing based Image Compression and Recovery

Compressive sensing is a new paradigm in image acquisition and compression. The CS theory promises recovery of images even if the sampling rate is far below the nyquist rate. This enables better acquisition and easy compression of images, which is more advantageous when the resources at the sender side are scarce. This paper shows the CS based compression and two recovery two methods i.e., l1 optimization and TSW CS recovery. Experimental results show that CS provides better compression, and TSWCS provides better recovery with less relative error recovery than l1 optimization. It is also observed that use of increased measurements leads to reduced error.