Pixel Recursive Super Resolution (1702.00783v2)

Abstract: We present a pixel recursive super resolution model that synthesizes realistic details into images while enhancing their resolution. A low resolution image may correspond to multiple plausible high resolution images, thus modeling the super resolution process with a pixel independent conditional model often results in averaging different details--hence blurry edges. By contrast, our model is able to represent a multimodal conditional distribution by properly modeling the statistical dependencies among the high resolution image pixels, conditioned on a low resolution input. We employ a PixelCNN architecture to define a strong prior over natural images and jointly optimize this prior with a deep conditioning convolutional network. Human evaluations indicate that samples from our proposed model look more photo realistic than a strong L2 regression baseline.

• The paper introduces a novel pixel recursive model that generates plausible high-resolution images from low-resolution inputs using an autoregressive, probabilistic approach.
• It employs a log-likelihood objective and adaptive sampling strategies to significantly outperform traditional super-resolution methods in perceptual quality.
• Human evaluations demonstrated that the model deceived observers at a higher rate than baselines, validating its effectiveness in capturing diverse image details.

Evaluation of the Pixel Recursive Super Resolution Model

The paper "Pixel Recursive Super Resolution," authored by Ryan Dahl, Mohammad Norouzi, and Jonathon Shlens, addresses a significant challenge in the field of computer vision and image processing: super-resolution with high magnification ratios. The work is premised on augmenting low-resolution images to produce plausible high-resolution outputs. In situations where inputs are particularly small and the required magnification is substantial, such as transforming an $8 \times 8$ image to a $32 \times 32$ image, traditional techniques often falter due to the inherent underspecification of the problem.

Key Contributions

The paper offers a novel deep learning architecture, extending PixelCNNs to tackle this challenging problem through a probabilistic approach. The key contributions of the paper are:

• Introducing a Pixel Recursive Model: The model synthesizes plausible high-resolution images by capturing the inherent multi-modality of the problem, accounting for the diverse, potential resolutions that could correspond to a given low-resolution input.
• Optimizing a Stochastic Framework: The method leverages an autoregressive structure where each pixel in the high-resolution output is conditioned not only on the low-resolution input but also on the previously generated pixels. This facilitates the generation of sharper images compared to traditional pixel-independent models, which often produce blurred outputs.
• Deployment of a Log-Likelihood Objective: The paper proposes a log-likelihood objective for training the model end-to-end, enabling the system to predict a variety of high-resolution images that correspond to a single low-resolution input image.
• Human Perception Studies: The effectiveness of the approach is demonstrated by human evaluation studies, which reveal that images produced by this model are successful in deceiving naive human observers more often than outputs of baseline models, including GANs and deep networks trained with mean squared error (MSE).

Experimental Setup and Findings

The model's performance was assessed using datasets containing images of faces (CelebA) and indoor scenes (LSUN Bedrooms). In addition to traditional benchmarks such as peak signal-to-noise ratio (pSNR) and structural similarity (SSIM), the paper also employed human evaluation to assess perceptual realism.

• Superiority Over Baselines: The pixel recursive super resolution model outperformed traditional methods such as bicubic interpolation, ResNet models trained with MSE, and adversarial models such as GANs in terms of human judgements on perceptual quality.
• Human Evaluation Metrics: The probabilistic model fooled human evaluators up to 27.9% of the time for bedrooms, significantly higher than the highest-performing baseline, which only succeeded 8.5% of the time.
• Image Generation Flexibility: Through adaptive sampling strategies, including the tuning of temperature parameters, the approach demonstrated control over the stochastic generation of high-resolution samples, optimizing output quality to align with human perceptual standards.

Implications and Future Directions

The implications of this work extend beyond the field of super-resolution. By addressing the multi-modality inherent in generating high-resolution images from low-resolution sources, this model fosters advancements in conditional generative models of images. The probabilistic framework could be extended to other applications requiring conditionally predictive modeling where uncertainty is a factor.

Moreover, the strong performance on perceptual tests underscores the importance of developing metrics that align more closely with human visual perceptions than traditional measures like pSNR and SSIM, which fail to capture the nuanced quality of synthesized details.

Future explorations may examine the integration of this super-resolution method with other computer vision tasks such as image inpainting or style transfer, where synthesizing high-fidelity visual content is critical. This paper sets a foundation for further research into scalable, probabilistic image synthesis, potentially impacting fields reliant on visual data enhancement, such as medical imaging, satellite imagery, and augmented reality.

In summary, "Pixel Recursive Super Resolution" represents a significant stride in modeling high-resolution image synthesis from low-resolution data, encapsulating the benefits of leveraging probabilistic models to uncover diverse image outputs that adhere to human visual expectations.