Learned Convolutional Sparse Coding
We propose a convolutional recurrent sparse auto-encoder model. The model consists of a sparse encoder, which is a convolutional extension of the learned ISTA (LISTA) method, and a linear convolutional decoder. Our strategy offers a simple method for learning a task-driven sparse convolutional dictionary (CD), and producing an approximate convolutional sparse code (CSC) over the learned dictionary. We trained the model to minimize reconstruction loss via gradient decent with back-propagation and have achieved competitive results to KSVD image denoising and to leading CSC methods in image inpainting requiring only a small fraction of their run-time.
Code (2)
Tasks
DecoderDenoisingImage DenoisingImage InpaintingSimilar Papers 제목 키워드 기반
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding
We propose a simple yet effective model for Single Image Super-Resolution (SISR), by combining the merits of Residual Learning and Convolutional Sparse Coding (RL-CSC). Our model is inspired by the Learned Iterative Shri…
Image Super-ResolutionSuper-ResolutionThe Interpretable Dictionary in Sparse Coding
Artificial neural networks (ANNs), specifically deep learning networks, have often been labeled as black boxes due to the fact that the internal representation of the data is not easily interpretable. In our work, we ill…
Deep LearningCompositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks
We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequently factorized to parse scene content.…
Scene ParsingFast and Flexible Convolutional Sparse Coding
Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opp…
General ClassificationEnergy-Based Spherical Sparse Coding
In this paper, we explore an efficient variant of convolutional sparse coding with unit norm code vectors where reconstruction quality is evaluated using an inner product (cosine distance). To use these codes for discrim…
ClassificationGeneral Classificationimage-classificationImage Classification