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Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of photon shot noise due to low-illumination and/or short exposure makes the deblurring task substantially more challenging than the conventional deblurring problems. In this paper we present an algorithm unrolling approach for the photon-limited deblurring problem by unrolling a Plug-and-Play algorithm for a fixed number of iterations. By introducing a three-operator splitting formation of the Plug-and-Play framework, we obtain a series of differentiable steps which allows the fixed iteration unrolled network to be trained end-to-end. The proposed algorithm demonstrates significantly better image recovery compared to existing state-of-the-art deblurring approaches. We also present a new photon-limited deblurring dataset for evaluating the performance of algorithms.

Over the ages technology has been occupying every field including the agriculture. Precision agriculture and Visual data mining uses technology to apply specific principles of data to interpret details like when and how much fertilizers to be used in a particular area (land). Data mining is the process of detecting patterns in a clustering form (certain chunk of information) to get more precise and accurate information. Not only it provides facilitating results but also improves the efficiency of farmer's productivity, it has also helped in qualitative improvement in the overall quality of life by providing timely and data inputs for decision making. Raw data collected from the statistics analysis has helped in determining the data to its full extent. This paper shows the techniques applied to real and till date agriculture data to reduce the high dimensional input data to much smaller size. We are using Self-organizing maps and multi-dimensional scaling techniques (Sammon's mapping) to reduce the data.