Deep Perceptual Enhancement for Medical Image Analysis.

Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practitioners notably complicated. This study proposes to enhance such low-quality images by incorporating end-to-end learning strategies for accelerating medical image analysis tasks. To the best concern, this is the first work in medical imaging which comprehensively tackles perceptual enhancement, including contrast correction, luminance correction, denoising, etc., with a fully convolutional deep network. The proposed network leverages residual blocks and a residual gating mechanism for diminishing visual artefacts and is guided by a multi-term objective function to perceive the perceptually plausible enhanced images. The practicability of the deep medical image enhancement method has been extensively investigated with sophisticated experiments. The experimental outcomes illustrate that the proposed method could outperform the existing enhancement methods for different medical image modalities by 5.00 to 7.00 dB in peak signal-to-noise ratio (PSNR) metrics and 4.00 to 6.00 in DeltaE metrics. Additionally, the proposed method can drastically improve the medical image analysis tasks' performance and reveal the potentiality of such an enhancement method in real-world applications.

Deep color reconstruction for a sparse color sensor.

Despite the advances in image sensors, mainstream RGB sensors are still struggling from low quantum efficiency due to the low sensitivity of the Bayer color filter array. To address this issue, a sparse color sensor uses mostly panchromatic white pixels and a smaller percentage of sparse color pixels to provide better low-light photography performance than a conventional Bayer RGB sensor. However, due to the lack of a proper color reconstruction method, sparse color sensors have not been developed thus far. This study proposes a deep-learning-based method for sparse color reconstruction that can realize such a sparse color sensor. The proposed color reconstruction method consists of a novel two-stage deep model followed by an adversarial training technique to reduce visual artifacts in the reconstructed color image. In simulations and experiments, visual results and quantitative comparisons demonstrate that the proposed color reconstruction method can outperform existing methods. In addition, a prototype system was developed using a hybrid color-plus-mono camera system. Experiments using the prototype system reveal the feasibility of a very sparse color sensor in different lighting conditions.