**Example 1**. (Lack of motivation)

Suppose that you receive a paper about image denoising. The goal of the paper is to tackle noise level greater than
200/255. Without reading any further, the number one question I would ask is under what situation will there be noise
greater than 200/255. (It could happen, but the authors have to justify.)

**Example 2**. (Unrealistic assumption)

Suppose that you receive a paper about image deblurring. The paper claims it
has a powerful deblurring method but it requires the knowledge of spatially
varying PSFs at every pixel. Without reading any further, the comment I have
would be that such assumption is too strong to hold in practice.

**Example 3**. (Known techniques in literature)

Suppose that you receive a paper about depth estimation. The authors claim
that they make new contributions by combining super-pixels, loopy belief
propagation, graph cut and hole-filling. But after reading the paper you find
that all methods are standard stuff in the literature. In this case, I would
point out the references that contain these techniques.

**Example 4**. (Incremental contribution)

Suppose that you receive a paper about compressive sensing. The authors claim
that they can tackle a Poisson model rather than the standard Gaussian model.
However, the only thing they change is to replace the Gaussian likelihood by
a Poisson likelihood. The body of the paper is a 5-page ADMM algorithm. In
this case, I would say that the contribution is incremental.

**Example 5**. (Flawed arguments)

Suppose that you receive a paper about video denoising under heavy noise. The
authors claim that they have a two stage approach. Step 1: Use motion
estimation to align the images; Step 2: Apply temporal filtering. I would be
very skeptical about the paper because when noise is heavy, no motion
estimation can provide accurate motion maps. So the temporal filtering will
be very problematic.

**Example 6**. (Insufficient experimetal support)

Suppose that you receive a paper about image super-resolution. The authors
show a 2x super-resolution result of Cameraman.tif with no noise. If I were
the reviewer I would ask how the method will perform on other images. I would
also ask about other testing configurations, e.g., noise level, 4x and 8x
super-resolution.