Recently, the JPEG standardization committee created an initiative called JPEG Pleno. "Pleno" is a reference to "plenoptic," a mathematical representation that not only provides information about any point within a scene but also about how it changes when observed from different positions. "Pleno" is also the Latin word for "complete," a reference to the JPEG committee's desire for future imaging to provide a more complete description of scenes, well beyond what's possible today. Here, we discuss the rationale behind the vision for the JPEG Pleno initiative and describe how it can potentially reinvent the future of imaging.

Up to now, an increase in camera resolution required image sensors with more and more pixels. However, acquisition systems are limited in their pixels per second throughput given as power and complexity constraints. Simply capturing more pixels in a given system is often not possible. We propose a new non-regular imaging architecture that samples only few pixels and reconstructs a high resolution image afterwards. Our sampling is optimized to provide non-regular spatial sampling from a sensor with regular readout circuits. An existing slow image acquisition system can then be used to capture the data. The image reconstruction is performed with a local sparsity-based approach. The result is a high resolution image that requires a much smaller effort during acquisition

We present a new method for capturing high dynamic range video (HDRV). Our method is based on spatially varying exposures, where individual pixels are covered with filters for different optical attenuation. For preventing the loss in resolution we use a new non- regular arrangement of the attenuation pattern. Subsequent image reconstruction based on the sparsity assumption allows the recon- struction of natural images with high detail. Index Terms High Dynamic Range Image Sensor, Digital Camera, Resolution Enhancement, Sparsit

Plenoptic cameras based on micro lens arrays as well as multi aperture cameras are able to capture a multitude of images with slightly shifted viewpoints. Although the amount of parallax between adjacent views is limited, precautions have to be taken in order to avoid alias when performing direct lightfield rendering. Against this background, we present an approach for the dense reconstruction of a lightfield based on a sparse lightfield acquired from a multi aperture camera with subsequent disparity estimation and depth image based view interpolation. Results show that the approach is suitable for all-in-focus-rendering

In high quality imaging even tiny distortions as small as a single pixel are visible and can not be accepted. Although the production quality of CMOS image sensors is very high, for reasonable yields we still need to accept some defect pixels and clusters of defects in large image sensors. In this paper we will compare compensation algorithms for raw image sensor data. We propose a new approach based on the sparsity assumption that outperforms existing defect compensation algorithms. Furthermore, our proposed interpolation algorithm is universal and not at all adapted to Bayer pattern images. It can directly be applied to any regular color filter pattern or gray scale image. Our examples show, that image sensors with large clusters of defects can still be used for the generation of high quality images