IMAGE DENOISING FOR IMAGE RETRIEVAL BY CASCADING A DEEP QUALITY ASSESSMENT NETWORK

Image denoising algorithms have evolved to optimize image quality as measured according to human visual perception. However, image denoising to maximize the success of computer vision algorithms operating on the denoised image has been much less investigated. We consider the problem of image denoising for Gaussian noise with respect to the specific application of image retrieval from a dataset. We define the notion of image quality as determined by the success of image retrieval and design a deep convolutional neural network (CNN) to predict this quality. This network is then cascaded with a deep CNN designed for image denoising, allowing for optimization of the denoising CNN to maximize retrieval performance. This framework allows us to couple denoising to the retrieval problem. We show through experiments on noisy images of the Oxford and Paris buildings datasets that such an approach yields improved mean average precision when compared to using denoising methods that are oblivious to the task of image retrieval.